Comparison of machine learning algorithms with regression analysis to predict the COVID-19 outbreak in Thailand

Coronavirus disease (COVID-19) is a public health problem in Thailand. Currently, there are more than 5 million infected people and the rate has been increasing at some point. It is therefore important to forecast the number of new cases over a short period of time to assist in strategic planning for the response to COVID-19. The purpose of this research paper was to compare the efficiency and prediction of the number of COVID-19 cases in Thailand using machine learning of 8 models using a regression analysis method. Using the 475-day dataset of COVID-19 cases in Thailand, the results showed that the predictive accuracy model (R2 score) from the testing dataset was the random forest (RF) model, which was 99.06%, followed by K-nearest neighbor (KNN), XGBoost. And the decision tree (DT) had the precision of 98.97, 98.67, and 98.64, respectively. And the results of the comparison of the number of infected people obtained from the prediction The models that predicted the number of real infections were the decision tree, random forest, and XGBoost, which were effective at predicting the number of infections correctly in the 2-4 day period. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

A Nomogram Model for Predicting the Polyphenol Content of Pu-Erh Tea.

To investigate different contents of pu-erh tea polyphenol affected by abiotic stress, this research determined the contents of tea polyphenol in teas produced by Yuecheng, a Xishuangbanna-based tea producer in Yunnan Province. The study drew a preliminary conclusion that eight factors, namely, altitude, nickel, available cadmium, organic matter, N, P, K, and alkaline hydrolysis nitrogen, had a considerable influence on tea polyphenol content with a combined analysis of specific altitudes and soil composition. The nomogram model constructed with three variables, altitude, organic matter, and P, screened by LASSO regression showed that the AUC of the training group and the validation group were respectively 0.839 and 0.750, and calibration curves were consistent. A visualized prediction system for the content of pu-erh tea polyphenol based on the nomogram model was developed and its accuracy rate, supported by measured data, reached 80.95%. This research explored the change of tea polyphenol content under abiotic stress, laying a solid foundation for further predictions for and studies on the quality of pu-erh tea and providing some theoretical scientific basis.

A Machine Learning Approach Reveals Distinct Predictors of Vaping Dependence for Adolescent Daily and Non-Daily Vapers in the COVID-19 Era.

Since 2016, there has been a substantial rise in e-cigarette (vaping) dependence among young people. In this prospective cohort study, we aimed to identify the different predictors of vaping dependence over 3 months among adolescents who were baseline daily and non-daily vapers. We recruited ever-vaping Canadian residents aged 16-25 years on social media platforms and asked them to complete a baseline survey in November 2020. A validated vaping dependence score (0-23) summing up their responses to nine questions was calculated at the 3-month follow-up survey. Separate lasso regression models were developed to identify predictors of higher 3-month vaping dependence score among baseline daily and non-daily vapers. Of the 1172 participants, 643 (54.9%) were daily vapers with a mean age of 19.6 ± 2.6 years and 76.4% (n = 895) of them being female. The two models achieved adequate predictive performance. Place of last vape purchase, number of days a pod lasts, and the frequency of nicotine-containing vaping were the most important predictors for dependence among daily vapers, while race, sexual orientation and reporting treatment for heart disease were the most important predictors in non-daily vapers. These findings have implications for vaping control policies that target adolescents at different stages of vape use.

Individual and environmental bicycling determinants during a pandemic.

Introduction: Research has identified many factors associated with bicycling, but little is known on their relative influence for an individual's decision to bicycle or what led to the surge in bicycling during the COVID-19 pandemic in the U.S. Methods: Our research leverages a sample of 6735 U.S. adults to identify key predictors and their relative influence on both increased bicycling during the pandemic and on whether an individual commutes by bicycle. LASSO regression models identified a reduced set of predictors for the outcomes of interest from 55 determinants included in the modeling. Results: We find individual and environmental factors have a role in explaining the shift towards bicycling-with key differences in predictors for increased overall cycling during the pandemic compared to bicycle commuting. Conclusions: Our findings add to the evidence base that policies can impact bicycling behavior. Specifically, increasing e-bike accessibility and limiting residential streets to local traffic are two policies that show promise for encouraging bicycling.

A LASSO-based model for financial distress of the Vietnamese listed firms: Does the covid-19 pandemic matter?

Financial distress is a vexing managerial challenge for businesses worldwide, especially during a turbulent period like the COVID-19 pandemic. Motivated by an increasing number of closed businesses in Vietnam during the recent COVID-19 pandemic, this study is conducted to provide a comprehensive analysis of financial distress for Vietnamese listed firms. Machine learning approaches are employed using the annual data of 492 listed firms from 2012 to 2021. Specifically, we aim to identify the appropriate distress predictors for the Vietnamese listed firms using LASSO, a technique known to be superior compared to other variable selection techniques. Empirical results reveal that there are four key financial distress predictors for the Vietnamese listed firms, namely the ratios of (i) working capital and total assets, (ii) retained earnings and total assets, (iii) earnings before interest and taxes and total assets and (iv) net income and total assets. We also conducted an industry-level analysis and found that the Energy sector experienced the highest number of financially distressed firms during Covid-19. In contrast, Communication Services, Health Care, and Utilities had the lowest number of distressed firms. Policy implications have emerged based on these important findings from our analysis.

Analysis and Prediction of Human Mobility in the United States during the Early Stages of the COVID-19 Pandemic using Regularized Linear Models.

Since the United States started grappling with the COVID-19 pandemic, with the highest number of confirmed cases and deaths in the world as of August 2020, most states have enforced travel restrictions resulting in drastic reductions in mobility and travel. However, the long-term implications of this crisis to mobility still remain uncertain. To this end, this study proposes an analytical framework that determines the most significant factors affecting human mobility in the United States during the early days of the pandemic. Particularly, the study uses least absolute shrinkage and selection operator (LASSO) regularization to identify the most significant variables influencing human mobility and uses linear regularization algorithms, including ridge, LASSO, and elastic net modeling techniques, to predict human mobility. State-level data were obtained from various sources from January 1, 2020 to June 13, 2020. The entire data set was divided into a training and a test data set, and the variables selected by LASSO were used to train models by the linear regularization algorithms, using the training data set. Finally, the prediction accuracy of the developed models was examined on the test data. The results indicate that several factors, including the number of new cases, social distancing, stay-at-home orders, domestic travel restrictions, mask-wearing policy, socioeconomic status, unemployment rate, transit mode share, percent of population working from home, and percent of older (60+ years) and African and Hispanic American populations, among others, significantly influence daily trips. Moreover, among all models, ridge regression provides the most superior performance with the least error, whereas both LASSO and elastic net performed better than the ordinary linear model.

Sovereign default network and currency risk premia.

We construct a sovereign default network by employing high-dimensional vector autoregressions obtained by analyzing connectedness in sovereign credit default swap markets. We develop four measures of centrality, namely, degree, betweenness, closeness, and eigenvector centralities, to detect whether network properties drive the currency risk premia. We observe that closeness and betweenness centralities can negatively drive currency excess returns but do not exhibit a relationship with forward spread. Thus, our developed network centralities are independent of an unconditional carry trade risk factor. Based on our findings, we develop a trading strategy by taking a long position on peripheral countries' currencies and a short position on core countries' currencies. The aforementioned strategy generates a higher Sharpe ratio than the currency momentum strategy. Our proposed strategy is robust to foreign exchange regimes and the coronavirus disease 2019 pandemic.

Effect of geographical factors on reference values of creatine kinase isoenzyme.

The aim of this study was to investigate the geographical spatial distribution of creatine kinase isoenzyme (CK-MB) in order to provide a scientific basis for clinical examination. The reference values of CK-MB of 8697 healthy adults in 137 cities in China were collected by reading a large number of literates. Moran index was used to determine the spatial relationship, and 24 factors were selected, which belonged to terrain, climate, and soil indexes. Correlation analysis was conducted between CK-MB and geographical factors to determine significance, and 9 significance factors were extracted. Based on R language to evaluate the degree of multicollinearity of the model, CK-MB Ridge model, Lasso model, and PCA model were established, through calculating the relative error to choose the best model PCA, testing the normality of the predicted values, and choosing the disjunctive kriging interpolation to make the geographical distribution. The results show that CK-MB reference values of healthy adults were generally correlated with latitude, annual sunshine duration, annual mean relative humidity, annual precipitation amount, and annual range of air temperature and significantly correlated with annual mean air temperature, topsoil gravel content, topsoil cation exchange capacity in clay, and topsoil cation exchange capacity in silt. The geospatial distribution map shows that on the whole, it is higher in the north and lower in the south, and gradually increases from the southeast coastal area to the northwest inland area. If the geographical factors are obtained in a location, the CK-MB model can be used to predict the CK-MB of healthy adults in the region, which provides a reference for us to consider regional differences in clinical diagnosis.

A machine learning approach to predicting bicycle demand during the COVID-19 pandemic

Count-based bicycle demand models have traditionally focused on estimation rather than prediction and have been criticized for lacking a direct causal relationship between significant variables and the activity being modeled. Because they are not choice-based models, they are doubted for their ability to forecast well. The rise of machine learning techniques has given researchers tools to build better predictive models, and the tools to evaluate predictiveness. Extensive previous work in the statistics and machine learning field has shown that the best predictive model is not synonymous with the most true (or explanatory) model. The non-motorized demand modeling community could leverage these lessons learned to develop better count-based predictive models. The rise of the COVID-19 pandemic has clearly affected travel patterns, and the broad data collection has opened-up an opportunity to leverage machine learning techniques to build a predictive bicycle demand model. This study uses bicycle count data, COVID-19 data, and weather data to develop a LASSO regression model for three facilities in Austin, TX. The COVID-19 variables included both state- and local-level data between March 15, 2020, and January 31, 2021. The final model selects six variables out of 28 variables and reveals that the increase of statewide COVID-19 fatalities, statewide molecular positivity rate, and local precipitation cause a decrease in bike ridership, meanwhile maximum temperature causes an increase. The LASSO model also has a lower prediction MSE during cross validation compared to the full model. This paper aims to bring to light that our present-day demand and volume forecasting efforts would benefit tremendously from a predictive modeling approach rather than valuing the most explanatory models as the only strong forecasters of demand. In the end, modelers can use this approach to improve the forecasting ability of any count-based bicycle demand model. © 2023

Quantile prediction for Bitcoin returns using financial assets' realized measures

This paper explores which properties of financial asset prices drive Bitcoin's return distributions, using quantile regressions with lagged realized moment measures of various financial assets. The result shows that Bitcoin's lagged realized volatility predicts its return distributions very well, revealing Bitcoin's aspect as a risk asset. Moreover, its lagged realized kurtosis plays some role in prediction in recent periods. In contrast, other financial assets' realized measures have limited predictive power, which implies the relative uniqueness of Bitcoin's price movements. Finally, out-of-sample predictions using lasso quantile regressions confirm the robust predictive power of lagged Bitcoin variables even in the Covid-19 period. © 2023 Elsevier Inc.

Spatio-temporal clustering analysis using generalized lasso with an application to reveal the spread of Covid-19 cases in Japan.

This study addressed the issue of determining multiple potential clusters with regularization approaches for the purpose of spatio-temporal clustering. The generalized lasso framework has flexibility to incorporate adjacencies between objects in the penalty matrix and to detect multiple clusters. A generalized lasso model with two L1 penalties is proposed, which can be separated into two generalized lasso models: trend filtering of temporal effect and fused lasso of spatial effect for each time point. To select the tuning parameters, the approximate leave-one-out cross-validation (ALOCV) and generalized cross-validation (GCV) are considered. A simulation study is conducted to evaluate the proposed method compared to other approaches in different problems and structures of multiple clusters. The generalized lasso with ALOCV and GCV provided smaller MSE in estimating the temporal and spatial effect compared to unpenalized method, ridge, lasso, and generalized ridge. In temporal effects detection, the generalized lasso with ALOCV and GCV provided relatively smaller and more stable MSE than other methods, for different structure of true risk values. In spatial effects detection, the generalized lasso with ALOCV provided higher index of edges detection accuracy. The simulation also suggested using a common tuning parameter over all time points in spatial clustering. Finally, the proposed method was applied to the weekly Covid-19 data in Japan form March 21, 2020, to September 11, 2021, along with the interpretation of dynamic behavior of multiple clusters.

Systemic risk and financial system network using financial risk meter: the case of Vietnam

In the financial market, systemic risk is defined as the possibility that an event at the company level could trigger severe instability or collapse of an entire industry or the whole economy. Thus, understanding systemic risk is crucial for the financial institutions, large corporations, investors and regulators. This article investigates systemic risk and spillover effect using the new Financial Risk Meter ((Formula presented.)) index, which is obtained from running quantile linear regression and Least Absolute Shrinkage and Selection Operator ((Formula presented.)) method. The (Formula presented.) index is obtained to identify the highly risky periods, the contributors to systemic risk and the potential activators of spillover effect. Moreover, interconnection between firms can be visualized as a network. We use a data set consisting of daily stock returns from 35 financial institutions and real estate firms in Vietnam, combined with 4 macroeconomic variables over the period from November 2011 to December 2020. The findings indicate that over the considered period, some detected highly risky periods are 2012, 2018 and 2020, probably due to the non-performing loan crisis in Vietnam, US-China trade war and global COVID-19 outbreak. Some active activators of risk spillover effect are also identified. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Estimations and Tests for Generalized Mediation Models with High-Dimensional Potential Mediators

Motivated by an empirical analysis of stock reaction to COVID-19 pandemic, we propose a generalized mediation model with high-dimensional potential mediators to study the mediation effects of financial metrics that bridge company's sector and stock value. We propose an estimation procedure for the direct effect via a partial penalized maximum likelihood method and establish its theoretical properties. We develop a Wald test for the indirect effect and show that the proposed test has a (Formula presented.) limiting null distribution. We also develop a partial penalized likelihood ratio test for the direct effect and show that the proposed test asymptotically follows a (Formula presented.) -distribution under null hypothesis. A more efficient estimator of indirect effect under complete mediation model is also developed. Simulation studies are conducted to examine the finite sample performance of the proposed procedures and compare with some existing methods. We further illustrate the proposed methodology with an empirical analysis of stock reaction to COVID-19 pandemic via exploring the underlying mechanism of the relationship between companies' sectors and their stock values. © 2023 American Statistical Association.

Optimal Knowledge Component Extracting Model for Knowledge-Concept Graph Completion in Education

As people have become accustomed to non-face-to-face education because of the COVID-19 pandemic, adaptive and personalized learning is being emphasized in the field of education. Learning paths suitable for each student may differ from those normally provided by teachers. To support coaching based on the concept of adaptive learning, the first step is to discover the relationships among the concepts in the curriculum provided in the form of a knowledge graph. In this study, feature reduction for the target knowledge-concept was first performed using Elastic Net and Random Forest algorithms, which are known to have the best performance in machine learning. Deep knowledge tracing (DKT) in the form of a dual-net, which is more efficient because of the already slimmer data, was then applied to increase the accuracy of feature selection. The new approach, termed the optimal knowledge component extracting (OKCE) model, was proven to be superior to a feature reduction approach using only Elastic Net and Random Forest using both open and commercial datasets. Finally, the OKCE model showed a meaningful knowledge-concept graph that could help teachers in adaptive and personalized learning. © 2013 IEEE.

LENGTH OF HOSPITAL STAY MODEL OF COVID-19 PATIENTS WITH QUANTILE BAYESIAN WITH PENALTY LASSO

This study aims to identify the best model for the length of hospital stay of COVID-19 patients in West Sumatra Province using Bayesian LASSO quantile regression method and Bayesian Adaptive LASSO quantile regression method. The quantile analysis is employed in Bayesian concept to produce more effective and natural estimated values, especially for data with non-normal distribution. The combination of the LASSO method and Adaptive LASSO as a variable selection method was applied to obtain the best model and produce estimated values that are close to the actual values. A comparison of the estimated values generated from the two methods was conducted using data from 1737 COVID-19 patients at M. Djamil General Hospital in Padang from March to December 2020. The result obtained is that the Bayesian Adaptive LASSO quantile regression method generally yields a shorter 95% confidence interval, with MAD (Median Absolute Deviation), MSE (Mean Squared Error), RMSE (Root of Mean Squared Error) values smaller than those produced by the Bayesian LASSO quantile regression method. The length of hospital stay of COVID-19 patients in West Sumatra is significantly influenced by age, the diagnosis of COVID-19 patients in the positive category, the patient's discharge status in the cured and death categories, and the number of comorbidities. Below the 0.50 quantile, the length of hospital stay for patients diagnosed with positive COVID-19 who were then declared cured is around three days and 4 hours longer than the length of stay for patients diagnosed with Person Under Supervision (PerUS). It is approximately 9 hours and 50 minutes longer than the length of stay of COVID-19 patients forced to go home. The length of stay of COVID-19 patients who died was around 16 hours 31 minutes shorter than the length of stay of COVID-19 patients who were forced to discharge from the hospital. © 2023the author(s).

Social Determinants of COVID-19 Vaccination Rates: A Time-Constrained Multiple Mediation Analysis.

Objective To estimate the multiple direct/indirect effects of social, environmental, and economic factors on COVID-19 vaccination rates (series complete) in the 3109 continental counties in the United States (U.S.). Study design  The dependent variable was the COVID-19 vaccination rates in the U.S. (April 15, 2022). Independent variables were collected from reliable secondary data sources, including the Census and CDC. Independent variables measured at two different time frames were utilized to predict vaccination rates. The number of vaccination sites in a given county was calculated using the geographic information system (GIS) packages as of April 9, 2022. The Internet Archive (Way Back Machine) was used to look up data for historical dates. Methods  A chain of temporally-constrained least absolute shrinkage and selection operator (LASSO) regressions was used to identify direct and indirect effects on vaccination rates. The first regression identified direct predictors of vaccination rates. Next, the direct predictors were set as response variables in subsequent regressions and regressed on variables that occurred before them. These regressions identified additional indirect predictors of vaccination. Finally, both direct and indirect variables were included in a network model. Results  Fifteen variables directly predicted vaccination rates and explained 43% of the variation in vaccination rates in April 2022. In addition, 11 variables indirectly affected vaccination rates, and their influence on vaccination was mediated by direct factors. For example, children in poverty rate mediated the effect of (a) median household income, (b) children in single-parent homes, and (c) income inequality. For another example, median household income mediated the effect of (a) the percentage of residents under the age of 18, (b) the percentage of residents who are Asian, (c) home ownership, and (d) traffic volume in the prior year. Our findings describe not only the direct but also the indirect effect of variables. Conclusions  A diverse set of demographics, social determinants, public health status, and provider characteristics predicted vaccination rates. Vaccination rates change systematically and are affected by the demographic composition and social determinants of illness within the county. One of the merits of our study is that it shows how the direct predictors of vaccination rates could be mediators of the effects of other variables.

Short-term forecasting of COVID-19 using support vector regression: An application using Zimbabwean data.

BACKGROUND: This study aims to show that including pairwise hierarchical interactions of covariates and combining forecasts from individual models improves prediction accuracy. METHODS: The least absolute shrinkage and selection operator via hierarchical pairwise interaction is used in selecting variables that are not correlated and with the greatest predictive power in single forecast models (Gradient boosting method [GBM], Generalized additive models [GAMs], Support vector regression [SVR]) are used in the analysis. The best model was selected based on the mean absolute error (MAE), the best key performance indicator for skewed data. Forecasts from the 5 models were combined using linear quantile regression averaging (LQRA). Box and Whiskers plots are used to diagnose the overall performance of fitted models. RESULTS: Single forecast models (GBM, GAMs, and SVRs) show that including pairwise interactions improves forecast accuracy. The SVR model with interactions based on the radial basis kernel function is the best from single forecast models with the lowest MAE. Combining point forecasts from all the single forecast models using the LQRA approach further reduces the MAE. However, based on the Box and Whiskers plot, the SVR model with pairwise interactions has the smallest range. CONCLUSIONS: Based on the key performance indicators, combining predictions from several individual models improves forecast accuracy. However, overall, the SVM with pairwise hierarchical interactions outperforms all the other models.

A COVID-19 elleni oltóanyagot elutasítók az aktív korú felnott magyar lakosság körében 2021 decemberében.

INTRODUCTION: In 2021, vaccines against COVID-19 became widely available in Hungary, but a part of the population refuses to be vaccinated, which hinders the control of the pandemic. OBJECTIVE: To explore the sociodemographic characteristics of the Hungarian vaccination-refusing population and to preliminarily explore the reasons behind their refusal. METHODS: In December 2021, survey data were collected online using quota-sampling among the Hungarian population aged 18-65 years with internet access. Sociodemographic variables, individual variables, and reasons for refusal were asked. 1905 completed questionnaires were included in this analysis. After variable selection using LASSO regression, binary logistic regression was used to identify the influencing variables. Reasons for rejection were examined both descriptively and using hierarchical classification. RESULTS: Respondents with lower income, lower education, females, younger age, people living in smaller municipalities and who perceived their own health as better were more likely to refuse vaccination. No similar associations were found with marital status, household size, life satisfaction and loneliness. Distrust of vaccination, safety concerns (especially side effects) and efficacy concerns are the main reasons for refusal, and to a lesser extent, the belief of immunity. CONCLUSIONS: Vaccination refusal is higher in vulnerable groups, which further increases their health risks. Alongside a well-designed health communication campaign, restoring trust in scientific and health institutions, transparent communication and a community-based approach appear to be important to increase vaccination uptake in Hungary. Orv Hetil. 2022; 163(29): 1135-1143.

A Nomogram for Predicting Prognosis of Advanced Schistosomiasis japonica in Dongzhi County-A Case Study.

BACKGROUNDS: Advanced schistosomiasis is the late stage of schistosomiasis, seriously jeopardizing the quality of life or lifetime of infected people. This study aimed to develop a nomogram for predicting mortality of patients with advanced schistosomiasis japonica, taking Dongzhi County of China as a case study. METHOD: Data of patients with advanced schistosomiasis japonica were collected from Dongzhi Schistosomiasis Hospital from January 2019 to July 2022. Data of patients were randomly divided into a training set and validation set with a ratio of 7:3. Candidate variables, including survival outcomes, demographics, clinical features, laboratory examinations, and ultrasound examinations, were analyzed and selected by LASSO logistic regression for the nomogram. The performance of the nomogram was assessed by concordance index (C-index), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). The calibration of the nomogram was evaluated by the calibration plots, while clinical benefit was evaluated by decision curve and clinical impact curve analysis. RESULTS: A total of 628 patients were included in the final analysis. Atrophy of the right liver, creatinine, ascites level III, N-terminal procollagen III peptide, and high-density lipoprotein were selected as parameters for the nomogram model. The C-index, sensitivity, specificity, PPV, and NPV of the nomogram were 0.97 (95% [CI]: [0.95-0.99]), 0.78 (95% [CI]: [0.64-0.87]), 0.97 (95% [CI]: [0.94-0.98]), 0.78 (95% [CI]: [0.64-0.87]), 0.97 (95% [CI]: [0.94-0.98]) in the training set; and 0.98 (95% [CI]: [0.94-0.99]), 0.86 (95% [CI]: [0.64-0.96]), 0.97 (95% [CI]: [0.93-0.99]), 0.79 (95% [CI]: [0.57-0.92]), 0.98 (95% [CI]: [0.94-0.99]) in the validation set, respectively. The calibration curves showed that the model fitted well between the prediction and actual observation in both the training set and validation set. The decision and the clinical impact curves showed that the nomogram had good clinical use for discriminating patients with high risk of death. CONCLUSIONS: A nomogram was developed to predict prognosis of advanced schistosomiasis. It could guide clinical staff or policy makers to formulate intervention strategies or efficiently allocate resources against advanced schistosomiasis.

Ventilator Pressure Prediction Using a Regularized Regression Model

The mechanical ventilation is one of the most frequent methods used in Intensive Care Units (ICUs) to improve the breathing of patients. During the early days of the COVID-19 pandemic, the use of mechanical ventilators has been crucial. In this work, we propose to build a Lasso regression model based on lung simulators for predicting the airway pressure in the respiratory circuit of ventilators while breathing. We model the whole breathing process in two separate states. After that, we analyze the feature importance in the regression model to better understand the ventilator pressure prediction. We anticipate this model would help improving the patient’s health and overcoming the cost barrier of new methods for mechanical ventilators. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.